Modern machine learning often optimizes a nonconvex objective
using simple algorithm such as gradient descent. One way of
explaining the success of such simple algorithms is by analyzing
the optimization landscape and show that all local minima
are...
Understanding phenomena in systems of many interacting quantum
particles, known as quantum many-body systems, is one of the most
sought-after objectives in contemporary physics research. The
challenge of simulating such systems lies in the extensive...
In this introductory seminar I will cover the main machine
learning techniques so-far adopted to study interacting quantum
systems. I will first introduce the concept of neural-network
quantum states [1], a representation of the many-body wave...
Most people interact with machine learning systems on a daily
basis. Such interactions often happen in strategic environments
where people have incentives to manipulate the learning algorithms.
As machine learning plays a more prominent role in our...
In various applications, one is given the advice or predictions
of several classifiers of unknown reliability, over multiple
questions or queries. This scenario is different from standard
supervised learning where classifier accuracy can be
assessed...
Linear dynamical systems are a continuous subclass of
reinforcement learning models that are widely used in robotics,
finance, engineering, and meteorology. Classical control, since the
works of Kalman, has focused on dynamics with Gaussian i.i.d...
An intriguing primitive representation, "expand-and-sparsify",
appears in the olfactory system of the fly and the sensory systems
of several other organisms. It maps an input vector to a much
higher-dimensional sparse representation, using a random...
“Deep learning” refers to use of neural networks to solve
learning problems, including “learning” hidden structures in large
and complex data sets. The theory for this field is still in its
infancy. Lately physical and biological scientists have...
